The promise, pitfalls, and path forward for AI in clinical assessments

| February 1, 2025

Artificial Intelligence (AI) is starting to reshape how we assess practical and clinical competency in nursing and other healthcare education. Picture a world where students receive immediate, tailored feedback on their skills without having to wait for busy human assessors. Imagine cost savings for universities no longer needing to hire endless rounds of clinically current professionals, and a level of objectivity and consistency in marking that surpasses human limitations.

In theory, AI could be the ultimate ally, helping identify learning trends, predict outcomes, and offer personalised guidance—anytime, anywhere. It’s no wonder universities are rubbing their hands together in delight, ready to embrace a new era where assessments are faster, fairer, and fiscally friendlier.

But before we break out the celebratory lamingtons, let’s take a step back. While AI may excel at crunching data and scoring tests, there are a few major hurdles to overcome. AI does not hold the requisite human qualifications to assess clinical competence, and it cannot (yet) replicate the intangible qualities at the heart of healthcare practice—empathy, intuition, and the nuanced judgement that comes from years of professional experience.

Current Australian accreditation standards make it clear that clinical assessments must be conducted by qualified, registered professionals. AI might be smart, but it isn’t a registered nurse or doctor. Relying on AI for assessment risks regulatory non-compliance and could undermine the validity of the qualifications awarded, potentially harming both institutional reputation and public trust.

Ethical and Legal Questions

There are also many ethical and legal questions. Healthcare graduates play a direct role in patient safety, so if an AI system erroneously awards competency to an underprepared practitioner, who is responsible? There’s also the spectre of bias. AI learns from the data it’s fed. If that data is skewed, the AI might grade certain student groups unfairly, compounding systemic inequalities.

Privacy is another serious issue. AI tools thrive on personal data, and with privacy laws tightening, ensuring secure data handling becomes paramount. Students have a right to understand and, if necessary, challenge the basis of their assessments—something that might prove difficult if an AI’s inner workings are as clear as mud. If a ‘black box’ algorithm decides a student’s fate, how do we guarantee transparency and procedural fairness?

These are not minor footnotes. The stakes are especially high in healthcare education, where the professional on the other end of the assessment is eventually responsible for real human lives. For now, accreditation standards remain inflexible. They require assessors who are not only qualified professionals but also up-to-date with current practice. AI, no matter how advanced, cannot claim that kind of professional currency.

Until these rules evolve, AI can only play a supplementary role at best. It might handle administrative tasks, provide initial automated feedback, or help highlight areas for human reviewers to examine more closely. There is the possibility of a hybrid model where AI flags common mistakes and suggests immediate pointers to human assessors who make the final call, exercising the empathetic, situational judgement that sets great nurses apart.

The Human Touch

This approach preserves the human touch while using of AI’s strengths. It could also pave the way for pilot programmes run in tandem with accrediting bodies. These trials could test how effectively AI can assist in assessment without sacrificing fairness, accuracy, or compliance.

Over time, if AI systems prove their reliability, professionalism, and freedom from bias, there’s a chance that the rules might adapt. Perhaps future accreditation frameworks will explicitly allow for AI-assisted assessments, granting institutions more flexibility and innovation in how they evaluate their students.

None of this will happen overnight. Legal considerations must be carefully addressed. Universities need to ensure that involving AI in assessments does not contravene consumer protection laws, privacy regulations, or anti-discrimination legislation. Students should know how AI is being used, provide informed consent, and have a clear avenue to appeal decisions. Institutions must guarantee that the technology they adopt is secure and that its outputs are explainable and justifiable.

In the meantime, AI should be seen as a promising tool, not a panacea. It can handle large volumes of marking and provide real-time feedback at all hours, broadening access and supporting students who juggle family commitments or shift work. This flexibility could foster greater inclusivity and accessibility in nursing education.

But nursing is both an art and a science. The empathy, moral reasoning, and clinical intuition that experienced nurse assessors provide cannot be replaced by an algorithm, no matter how sophisticated. The best outcomes will come from a partnership—employing AI to handle routine tasks and boost efficiency while leaving the high-level, nuanced judgements to qualified humans.

Yes, AI can save universities money, time, and effort, and it can improve consistency and speed. But until regulations evolve, ethical dilemmas are resolved, and technical issues are ironed out, AI will remain a helpful apprentice rather than the master of the assessment arena. AI can transform nursing education, but only if introduced thoughtfully, responsibly, and in a way that respects the complexity and humanity at the core of the nursing profession.

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